We study how the dependence of a simulation output on an uncertain parameter can be determined, when simulations are computationally expensive and so can only be run for very few p...
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
We design two different strategies for computing the unknown content preferences in an online social network based on a small set of nodes in the corresponding social graph for wh...
Consider the decomposition of a signal into features that undergo transformations drawn from a continuous family. Current methods discretely sample the transformations and apply s...
Chaitanya Ekanadham, Daniel Tranchina, Eero P. Sim...
Multi-tensor models provide information about the fiber bundles underlying characteristics and are of great interest for clinical applications. In this work we propose both a nov...